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Rolling bearing fault detection using continuous deep belief network with locally linear embedding

Research article (Computers in Industry, 2018) · cited 182× · AI/ML
academia openalex:W2793039588
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Rolling bearing fault detection using continuous deep belief network with locally linear embedding

Summary

Rolling bearing fault detection using continuous deep belief network with locally linear embedding is a scholarly article[1].

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  • Rolling bearing fault detection using continuous deep belief network with locally linear embedding's instance of is recorded as scholarly article[2].

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  1. [2] ↑ . wikidata.org.

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  1. [1] ↑ . Wikidata. wikidata.org.

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APA 4ort.xyz Knowledge Graph. (2026). Rolling bearing fault detection using continuous deep belief network with locally linear embedding. Retrieved May 24, 2026, from https://4ort.xyz/entity/rolling-bearing-fault-detection-using-continuous-deep-belief-network-with-locally-linear-embedding
MLA “Rolling bearing fault detection using continuous deep belief network with locally linear embedding.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/rolling-bearing-fault-detection-using-continuous-deep-belief-network-with-locally-linear-embedding.
BibTeX @misc{4ortxyz_rolling-bearing-fault-detection-using-continuous-deep-belief-network-with-locally-linear-embedding_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Rolling bearing fault detection using continuous deep belief network with locally linear embedding}}, year = {2026}, url = {https://4ort.xyz/entity/rolling-bearing-fault-detection-using-continuous-deep-belief-network-with-locally-linear-embedding}, note = {Accessed: 2026-05-24}}
LLM prompt According to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Rolling bearing fault detection using continuous deep belief network with locally linear embedding — https://4ort.xyz/entity/rolling-bearing-fault-detection-using-continuous-deep-belief-network-with-locally-linear-embedding (retrieved 2026-05-24)

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